Distributed Model Predictive Control of Epidemics via Transportation Flow Regulations

Yuhong Chen; Xiaobing Dai; Sihua Zhang; Martin Buss; Sandra Hirche; Fangzhou Liu

doi:10.1016/j.ifacol.2025.07.062

Distributed Model Predictive Control of Epidemics via Transportation Flow Regulations

Yuhong Chen
, Xiaobing Dai^*
, Sihua Zhang
, Martin Buss
, Sandra Hirche
, Fangzhou Liu

^*Corresponding author for this work

School of Astronautics

Technical University of Munich

Research output: Contribution to journal › Conference article › peer-review

1 Scopus citations

Abstract

The COVID-19 pandemic highlights the necessity of epidemic control while minimizing societal and economic disruption. This paper formulates an optimal transportation flow restriction problem that offers a more direct and realistic representation of real-world policy design. A sequential distributed Lyapunov-based MPC (SDLMPC) approach is introduced, enabling asynchronous decision-making for subpopulations. The feasibility of applying SDLMPC is proven, and its effectiveness is validated through simulations using real COVID-19 data from Germany. The results demonstrate that SDLMPC effectively balances epidemic control with reduced negative impacts, offering a practical and implementable decision-making framework.

Original language	English
Pages (from-to)	163-168
Number of pages	6
Journal	IFAC-PapersOnLine
Volume	59
Issue number	4
DOIs	https://doi.org/10.1016/j.ifacol.2025.07.062
State	Published - 1 Jun 2025
Event	10th IFAC Conference on Networked Systems, NECSYS 2025 - Hong Kong, Hong Kong Duration: 2 Jun 2025 → 5 Jun 2025

Keywords

COVID-19
Epidemics
distributed model predictive control
transportation restriction

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10.1016/j.ifacol.2025.07.062

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title = "Distributed Model Predictive Control of Epidemics via Transportation Flow Regulations",

abstract = "The COVID-19 pandemic highlights the necessity of epidemic control while minimizing societal and economic disruption. This paper formulates an optimal transportation flow restriction problem that offers a more direct and realistic representation of real-world policy design. A sequential distributed Lyapunov-based MPC (SDLMPC) approach is introduced, enabling asynchronous decision-making for subpopulations. The feasibility of applying SDLMPC is proven, and its effectiveness is validated through simulations using real COVID-19 data from Germany. The results demonstrate that SDLMPC effectively balances epidemic control with reduced negative impacts, offering a practical and implementable decision-making framework.",

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AU - Chen, Yuhong

AU - Dai, Xiaobing

AU - Zhang, Sihua

AU - Buss, Martin

AU - Hirche, Sandra

AU - Liu, Fangzhou

PY - 2025/6/1

Y1 - 2025/6/1

N2 - The COVID-19 pandemic highlights the necessity of epidemic control while minimizing societal and economic disruption. This paper formulates an optimal transportation flow restriction problem that offers a more direct and realistic representation of real-world policy design. A sequential distributed Lyapunov-based MPC (SDLMPC) approach is introduced, enabling asynchronous decision-making for subpopulations. The feasibility of applying SDLMPC is proven, and its effectiveness is validated through simulations using real COVID-19 data from Germany. The results demonstrate that SDLMPC effectively balances epidemic control with reduced negative impacts, offering a practical and implementable decision-making framework.

AB - The COVID-19 pandemic highlights the necessity of epidemic control while minimizing societal and economic disruption. This paper formulates an optimal transportation flow restriction problem that offers a more direct and realistic representation of real-world policy design. A sequential distributed Lyapunov-based MPC (SDLMPC) approach is introduced, enabling asynchronous decision-making for subpopulations. The feasibility of applying SDLMPC is proven, and its effectiveness is validated through simulations using real COVID-19 data from Germany. The results demonstrate that SDLMPC effectively balances epidemic control with reduced negative impacts, offering a practical and implementable decision-making framework.

KW - COVID-19

KW - Epidemics

KW - distributed model predictive control

KW - transportation restriction

UR - https://www.scopus.com/pages/publications/105012440112

U2 - 10.1016/j.ifacol.2025.07.062

DO - 10.1016/j.ifacol.2025.07.062

M3 - 会议文章

AN - SCOPUS:105012440112

SN - 2405-8971

VL - 59

SP - 163

EP - 168

JO - IFAC-PapersOnLine

JF - IFAC-PapersOnLine

IS - 4

T2 - 10th IFAC Conference on Networked Systems, NECSYS 2025

Y2 - 2 June 2025 through 5 June 2025

ER -

Distributed Model Predictive Control of Epidemics via Transportation Flow Regulations

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